Efficient uneven-lighting image binarization by support vector machines

Abstract An uneven-lighting image binarization technique using support vector machines (SVM) is proposed. High-pass filtering first transforms the grayscale image into a contour image, which categorizes some pixels as edge pixels. The grayscale image is partitioned into numerous blocks. In each block, the edge pixel with largest gradient is designated as the feature edge pixel of the block, and its feature white pixel is then synthesized based on the feature black pixel. The features of a feature pixel can include its coordinates, gray level and even gradient. The trained SVM can accurately binarize the grayscale image even with uneven lighting disturbance.

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